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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1001–1050 of 2050 papers

TitleStatusHype
Fast Instrument Learning with Faster RatesCode0
Fusion Subspace Clustering for Incomplete Data—0
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems—0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
Interpretable Machine Learning for Self-Service High-Risk Decision-Making—0
Differentially Private Generalized Linear Models Revisited—0
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained ModelsCode0
Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net—0
Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual TransferCode0
Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts—0
Differentially Private Learning with Margin Guarantees—0
Improved Group Robustness via Classifier Retraining on Independent SplitsCode0
Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion—0
Trinary Tools for Continuously Valued Binary Classifiers—0
Sparse Interaction Neighborhood Selection for Markov Random Fields via Reversible Jump and PseudoposteriorsCode0
LaF: Labeling-Free Model Selection for Automated Deep Neural Network ReusingCode0
Towards Fair Evaluation of Dialogue State Tracking by Flexible Incorporation of Turn-level PerformancesCode0
Statistical Model Criticism of Variational Auto-Encoders—0
Consensual Aggregation on Random Projected High-dimensional Features for Regression—0
Fundamental limits to learning closed-form mathematical models from data—0
Pareto-optimal clustering with the primal deterministic information bottleneckCode0
System Identification via Nuclear Norm RegularizationCode0
Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice—0
Black-box Selective Inference via Bootstrapping—0
Tutorial: Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System—0
A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization—0
Predictor Selection for Synthetic Controls—0
On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging—0
Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work—0
Mixture Components Inference for Sparse Regression: Introduction and Application for Estimation of Neuronal Signal from fMRI BOLD—0
Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management—0
Sampling Bias Correction for Supervised Machine Learning: A Bayesian Inference Approach with Practical Applications—0
Bayesian Spatial Predictive Synthesis—0
Geometric and Topological Inference for Deep Representations of Complex Networks—0
Nonlinear Isometric Manifold Learning for Injective Normalizing Flows—0
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion—0
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis—0
Gaussian Process-based Spatial Reconstruction of Electromagnetic fields—0
A study on the distribution of social biases in self-supervised learning visual models—0
Regularized Bilinear Discriminant Analysis for Multivariate Time Series Data—0
Ensemble Method for Estimating Individualized Treatment Effects—0
Exploratory Hidden Markov Factor Models for Longitudinal Mobile Health Data: Application to Adverse Posttraumatic Neuropsychiatric Sequelae—0
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition—0
Efficient Distributed DNNs in the Mobile-edge-cloud Continuum—0
Cyclical Variational Bayes Monte Carlo for Efficient Multi-Modal Posterior Distributions Evaluation—0
Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks—0
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants—0
Embarrassingly Simple Performance Prediction for Abductive Natural Language InferenceCode0
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomesCode0
Modeling High-Dimensional Data with Unknown Cut Points: A Fusion Penalized Logistic Threshold RegressionCode0
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